Files

T

shaoxiongduan c6d0233eab [bugfix] WanTrack: track subsampling was a no-op when max_points == N

_augment_tracks gated the object-coverage + low-rank-weighted + uniform draw on
`0 < max_points < N`, so with the default WANTRACK_MAX_POINTS=2500 and the full
2500-track grid (max_points == N) the entire subsample block was skipped -- the
model saw all 2500 tracks every step and only chunk-masking ran. Gate on
`min_points < N` instead so K~U[1000,2500] actually samples (verified: kept
1179/1437/1573 across steps, was a constant 2500).

Also: TrackValidation now applies the same sampler (loads object_ids/track_weights,
runs _augment_tracks deterministically) so validation is conditioned on -- and
overlays -- the sampled subset, not the full grid. Env-gated WANTRACK_DEBUG=1 logs
per-step sampling stats (kept count, object coverage, coord ranges). Adds a
single-video (golf) overfit config for debugging.

2026-07-03 08:18:44 +00:00

configs

[feat] Add Wan RL DiffusionNFT training (#1450 )

2026-06-11 21:18:59 -07:00

scenario

[bugfix] WanTrack: track subsampling was a no-op when max_points == N

2026-07-03 08:18:44 +00:00

README.md

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

run_slurm.sh

[misc]: reorganize training configs and add documentation (#1177 )

2026-03-26 16:38:31 -07:00

run.sh

[feat] Add minimal LoRA finetuning support to the YAML training stack (#1242 )

2026-05-17 14:49:13 -07:00

README.md

Training

Quick Start

Single-node

bash examples/train/run.sh <config.yaml> [--dotted.key value ...]

The script auto-detects available GPUs. Override with NUM_GPUS:

NUM_GPUS=4 bash examples/train/run.sh \
    examples/train/configs/fine_tuning/wan/t2v.yaml

Multi-node (Slurm)

bash examples/train/run_slurm.sh <config.yaml> <num_nodes> [--dotted.key value ...]

bash examples/train/run_slurm.sh \
    examples/train/configs/fine_tuning/wan/t2v.yaml 4 \
    --training.distributed.hsdp_shard_dim 8 \
    --training.distributed.hsdp_replicate_dim 4

Slurm environment variables:

Variable	Default	Description
`PARTITION`	`main`	Slurm partition
`NUM_GPUS`	`8`	GPUs per node
`CPUS_PER_TASK`	`128`	CPUs per task
`MEM`	`1440G`	Memory per node
`EXCLUDE`	`""`	Nodes to exclude

Overriding config values

Any config field can be overridden from the command line using dotted keys:

bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
    --training.optimizer.learning_rate 1e-5 \
    --training.loop.max_train_steps 1000 \
    --training.checkpoint.output_dir outputs/my_experiment

Resuming from a checkpoint

bash examples/train/run.sh examples/train/configs/fine_tuning/wan/t2v.yaml \
    --training.checkpoint.resume_from_checkpoint outputs/my_experiment/checkpoint-500

W&B Logging

Training metrics and validation videos are logged to Weights & Biases. Set your API key before launching:

export WANDB_API_KEY=your_key_here

To disable W&B (e.g. for local debugging):

export WANDB_MODE=offline

The project name and run name are set in the config under training.tracker:

training:
  tracker:
    project_name: my_project
    run_name: my_run

Directory Layout

examples/train/
├── configs/      # Single-step training configs (by method and model)
├── scenario/     # Multi-step end-to-end training pipelines
├── run.sh        # Single-node launcher
└── run_slurm.sh  # Multi-node Slurm launcher

See configs/README.md and scenario/README.md for details.